基于光诱导热弹性光谱和深度学习的双组件气体传感器.
Jinfeng Hou1, Xiaonan Liu1, Haiyue Sun1
1National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150000, China.
Analytical chemistry
|February 27, 2025
概括
这项研究引入了一种新的双组件气体传感器,用于检测乙和二氧化碳,使用光诱导热弹性光谱和深度学习模型. 先进的SSA-CNN-BiGRU-Attention模型在气体度逆转方面实现了高精度.
科学领域:
- 频谱学是一种光谱学.
- 气体传感器是指气体传感器.
- 人工智能的人工智能
背景情况:
- 精确检测多个气体成分,特别是重叠的光谱线,在环境监测和工业安全方面仍然是一个挑战.
- 光诱导热弹性光谱 (LITES) 为气体检测提供了一种敏感的方法,但对于复杂的混合物需要复杂的数据分析.
研究的目的:
- 开发和验证一种新的双组件气体传感器,用于同时检测乙 (C2H2) 和二氧化碳 (CO2).
- 调查综合深度学习模型 (SSA-CNN-BiGRU-Attention) 对于精确的气体度逆转的有效性,特别是在光谱线重叠的条件下.
主要方法:
- 采用光诱导热弹性光谱法,使用两个激光器 (1530nm和1577nm) 激发C2H2和CO2分子.
- 开发了一种混合深度学习模型,集成了Sparrow搜索算法 (SSA),卷积神经网络 (CNN),双向门式反复单元 (BiGRU) 和注意力机制.
- 应用该模型在三个不同程度的光谱线重叠的场景中逆转气体度.
主要成果:
- SSA-CNN-BiGRU-Attention模型在度逆转方面表现出高精度,在测试组中,R平方值超过0.99.
- 在双组件气体检测中实现了显著降低的平均相对误差 (MRE) 低于1.2%.
- 该模型有效地根据第二波 (2f) 信号特征分配权重,优化参数选择.
结论:
- 开发的双组件气体传感器和SSA-CNN-BiGRU-Attention模型为准确的气体度逆转提供了强大的解决方案,即使有光谱重叠.
- 这项工作为处理多元组件气体传感中的光谱线重叠提供了有价值的见解.
- 这种方法对未来用于检测和量化更复杂的气体混合物的应用是有希望的.
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